mlr_learners_classif.lightgbm: Classification LightGBM Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Classification LightGBM Learner

Description

Gradient boosting algorithm. Calls lightgbm::lightgbm() from lightgbm. The list of parameters can be found here and in the documentation of lightgbm::lgb.train(). Note that lightgbm models have to be saved using lightgbm::lgb.save, so you cannot simpliy save the learner using saveRDS. This will change in future versions of lightgbm.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.lightgbm")
lrn("classif.lightgbm")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3extralearners, lightgbm

Parameters

Id	Type	Default	Levels	Range
num_iterations	integer	100		[0, \infty)
objective	character	-	binary, multiclass, multiclassova	-
eval	untyped	-		-
verbose	integer	1		(-\infty, \infty)
record	logical	TRUE	TRUE, FALSE	-
eval_freq	integer	1		[1, \infty)
early_stopping_rounds	integer	-		[1, \infty)
early_stopping	logical	FALSE	TRUE, FALSE	-
callbacks	untyped	-		-
reset_data	logical	FALSE	TRUE, FALSE	-
categorical_feature	untyped	""		-
convert_categorical	logical	TRUE	TRUE, FALSE	-
boosting	character	gbdt	gbdt, rf, dart, goss	-
linear_tree	logical	FALSE	TRUE, FALSE	-
learning_rate	numeric	0.1		[0, \infty)
num_leaves	integer	31		[1, 131072]
tree_learner	character	serial	serial, feature, data, voting	-
num_threads	integer	0		[0, \infty)
device_type	character	cpu	cpu, gpu	-
seed	integer	-		(-\infty, \infty)
deterministic	logical	FALSE	TRUE, FALSE	-
data_sample_strategy	character	bagging	bagging, goss	-
force_col_wise	logical	FALSE	TRUE, FALSE	-
force_row_wise	logical	FALSE	TRUE, FALSE	-
histogram_pool_size	numeric	-1		(-\infty, \infty)
max_depth	integer	-1		(-\infty, \infty)
min_data_in_leaf	integer	20		[0, \infty)
min_sum_hessian_in_leaf	numeric	0.001		[0, \infty)
bagging_fraction	numeric	1		[0, 1]
pos_bagging_fraction	numeric	1		[0, 1]
neg_bagging_fraction	numeric	1		[0, 1]
bagging_freq	integer	0		[0, \infty)
bagging_seed	integer	3		(-\infty, \infty)
feature_fraction	numeric	1		[0, 1]
feature_fraction_bynode	numeric	1		[0, 1]
feature_fraction_seed	integer	2		(-\infty, \infty)
extra_trees	logical	FALSE	TRUE, FALSE	-
extra_seed	integer	6		(-\infty, \infty)
first_metric_only	logical	FALSE	TRUE, FALSE	-
max_delta_step	numeric	0		(-\infty, \infty)
lambda_l1	numeric	0		[0, \infty)
lambda_l2	numeric	0		[0, \infty)
linear_lambda	numeric	0		[0, \infty)
min_gain_to_split	numeric	0		[0, \infty)
drop_rate	numeric	0.1		[0, 1]
max_drop	integer	50		(-\infty, \infty)
skip_drop	numeric	0.5		[0, 1]
xgboost_dart_mode	logical	FALSE	TRUE, FALSE	-
uniform_drop	logical	FALSE	TRUE, FALSE	-
drop_seed	integer	4		(-\infty, \infty)
top_rate	numeric	0.2		[0, 1]
other_rate	numeric	0.1		[0, 1]
min_data_per_group	integer	100		[1, \infty)
max_cat_threshold	integer	32		[1, \infty)
cat_l2	numeric	10		[0, \infty)
cat_smooth	numeric	10		[0, \infty)
max_cat_to_onehot	integer	4		[1, \infty)
top_k	integer	20		[1, \infty)
monotone_constraints	untyped	NULL		-
monotone_constraints_method	character	basic	basic, intermediate, advanced	-
monotone_penalty	numeric	0		[0, \infty)
feature_contri	untyped	NULL		-
forcedsplits_filename	untyped	""		-
refit_decay_rate	numeric	0.9		[0, 1]
cegb_tradeoff	numeric	1		[0, \infty)
cegb_penalty_split	numeric	0		[0, \infty)
cegb_penalty_feature_lazy	untyped	-		-
cegb_penalty_feature_coupled	untyped	-		-
path_smooth	numeric	0		[0, \infty)
interaction_constraints	untyped	-		-
use_quantized_grad	logical	TRUE	TRUE, FALSE	-
num_grad_quant_bins	integer	4		(-\infty, \infty)
quant_train_renew_leaf	logical	FALSE	TRUE, FALSE	-
stochastic_rounding	logical	TRUE	TRUE, FALSE	-
serializable	logical	TRUE	TRUE, FALSE	-
max_bin	integer	255		[2, \infty)
max_bin_by_feature	untyped	NULL		-
min_data_in_bin	integer	3		[1, \infty)
bin_construct_sample_cnt	integer	200000		[1, \infty)
data_random_seed	integer	1		(-\infty, \infty)
is_enable_sparse	logical	TRUE	TRUE, FALSE	-
enable_bundle	logical	TRUE	TRUE, FALSE	-
use_missing	logical	TRUE	TRUE, FALSE	-
zero_as_missing	logical	FALSE	TRUE, FALSE	-
feature_pre_filter	logical	TRUE	TRUE, FALSE	-
pre_partition	logical	FALSE	TRUE, FALSE	-
two_round	logical	FALSE	TRUE, FALSE	-
forcedbins_filename	untyped	""		-
is_unbalance	logical	FALSE	TRUE, FALSE	-
scale_pos_weight	numeric	1		[0, \infty)
sigmoid	numeric	1		[0, \infty)
boost_from_average	logical	TRUE	TRUE, FALSE	-
eval_at	untyped	1:5		-
multi_error_top_k	integer	1		[1, \infty)
auc_mu_weights	untyped	NULL		-
num_machines	integer	1		[1, \infty)
local_listen_port	integer	12400		[1, \infty)
time_out	integer	120		[1, \infty)
machines	untyped	""		-
gpu_platform_id	integer	-1		(-\infty, \infty)
gpu_device_id	integer	-1		(-\infty, \infty)
gpu_use_dp	logical	FALSE	TRUE, FALSE	-
num_gpu	integer	1		[1, \infty)
start_iteration_predict	integer	0		(-\infty, \infty)
num_iteration_predict	integer	-1		(-\infty, \infty)
pred_early_stop	logical	FALSE	TRUE, FALSE	-
pred_early_stop_freq	integer	10		(-\infty, \infty)
pred_early_stop_margin	numeric	10		(-\infty, \infty)

Initial parameter values

• num_threads:

  • Actual default: 0L

  • Initial value: 1L

  • Reason for change: Prevents accidental conflicts with future.

• verbose:

  • Actual default: 1L

  • Initial value: -1L

  • Reason for change: Prevents accidental conflicts with mlr messaging system.

Custom mlr3 defaults

• objective: Depending if the task is binary / multiclass, the default is "binary" or "multiclasss".

Custom mlr3 parameters

• early_stopping Whether to use the test set for early stopping. Default is FALSE.

• convert_categorical: Additional parameter. If this parameter is set to TRUE (default), all factor and logical columns are converted to integers and the parameter categorical_feature of lightgbm is set to those columns.

• num_class: This parameter is automatically inferred for multiclass tasks and does not have to be set.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLightGBM

Methods

Public methods

• LearnerClassifLightGBM$new()

• LearnerClassifLightGBM$importance()

• LearnerClassifLightGBM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLightGBM$new()

---

Method importance()

The importance scores are extracted from lbg.importance.

Usage

LearnerClassifLightGBM$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLightGBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

kapsner

References

Ke, Guolin, Meng, Qi, Finley, Thomas, Wang, Taifeng, Chen, Wei, Ma, Weidong, Ye, Qiwei, Liu, Tie-Yan (2017). “Lightgbm: A highly efficient gradient boosting decision tree.” Advances in neural information processing systems, 30.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

learner = mlr3::lrn("classif.lightgbm")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.